examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java
<reponame>LexCC/spark-py/*
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 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
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// scalastyle:off println
package org.apache.spark.examples.ml;
// $example on$
import org.apache.spark.ml.Pipeline;
import org.apache.spark.ml.PipelineModel;
import org.apache.spark.ml.PipelineStage;
import org.apache.spark.ml.classification.DecisionTreeClassifier;
import org.apache.spark.ml.classification.DecisionTreeClassificationModel;
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
import org.apache.spark.ml.feature.*;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
// $example off$

public class JavaDecisionTreeClassificationExample {
  public static void main(String[] args) {
    SparkSession spark = SparkSession
      .builder()
      .appName("JavaDecisionTreeClassificationExample")
      .getOrCreate();

    // $example on$
    // Load the data stored in LIBSVM format as a DataFrame.
    Dataset<Row> data = spark
      .read()
      .format("libsvm")
      .load("data/mllib/sample_libsvm_data.txt");

    // Index labels, adding metadata to the label column.
    // Fit on whole dataset to include all labels in index.
    StringIndexerModel labelIndexer = new StringIndexer()
      .setInputCol("label")
      .setOutputCol("indexedLabel")
      .fit(data);

    // Automatically identify categorical features, and index them.
    VectorIndexerModel featureIndexer = new VectorIndexer()
      .setInputCol("features")
      .setOutputCol("indexedFeatures")
      .setMaxCategories(4) // features with > 4 distinct values are treated as continuous.
      .fit(data);

    // Split the data into training and test sets (30% held out for testing).
    Dataset<Row>[] splits = data.randomSplit(new double[]{0.7, 0.3});
    Dataset<Row> trainingData = splits[0];
    Dataset<Row> testData = splits[1];

    // Train a DecisionTree model.
    DecisionTreeClassifier dt = new DecisionTreeClassifier()
      .setLabelCol("indexedLabel")
      .setFeaturesCol("indexedFeatures");

    // Convert indexed labels back to original labels.
    IndexToString labelConverter = new IndexToString()
      .setInputCol("prediction")
      .setOutputCol("predictedLabel")
      .setLabels(labelIndexer.labelsArray()[0]);

    // Chain indexers and tree in a Pipeline.
    Pipeline pipeline = new Pipeline()
      .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter});

    // Train model. This also runs the indexers.
    PipelineModel model = pipeline.fit(trainingData);

    // Make predictions.
    Dataset<Row> predictions = model.transform(testData);

    // Select example rows to display.
    predictions.select("predictedLabel", "label", "features").show(5);

    // Select (prediction, true label) and compute test error.
    MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
      .setLabelCol("indexedLabel")
      .setPredictionCol("prediction")
      .setMetricName("accuracy");
    double accuracy = evaluator.evaluate(predictions);
    System.out.println("Test Error = " + (1.0 - accuracy));

    DecisionTreeClassificationModel treeModel =
      (DecisionTreeClassificationModel) (model.stages()[2]);
    System.out.println("Learned classification tree model:\n" + treeModel.toDebugString());
    // $example off$

    spark.stop();
  }
}
